Conventional inspection and examination of materials using ultrasound typically employs processing of the raw radio frequency (RF) ultrasound signal at discrete “snapshots” in time, for example, to create B-scan images. Such images are widely used in fields such as medicine; however, evidence suggests that they may be of limited utility in certain applications, particularly where fine resolution of tissue structure is required for accurate classification, such as in-detecting structural differences among biological tissues, as may be required in diagnosing various cancers.
Several researchers have studied ultrasound-based solutions for computer-aided diagnosis of cancer. The first-order statistical moments (such as mean, standard deviation, skewness and kurtosis) of the intensities of pixels in each region of interest (ROI) of the tissue form a basic set of features for tissue classification [5, 6]. Tissue characterization based on the acoustic parameters extracted from the raw RF ultrasound echo signals (before being transformed to B-scan images) has been studied since the early 1970's (see [7] for a review). Frequency-dependent nature of ultrasound scattering and attenuation phenomena can characterize different tissue types and is studied through frequency spectrum of RF signals. Along with texture and co-occurrence based features extracted from B-scan images, RF spectrum parameters have been used to form hybrid feature vectors to be used for detection of cancer [20]. Such features are utilized as the input to neural networks and neuro-fuzzy inference systems [5], self organizing Kohonen maps [8] and quadratic Bayes classifiers [9] for characterization of tissue. Nevertheless, despite the long history of studies in this field, an accurate analytical model of ultrasound-tissue interactions is still outstanding [9, 10] and the results of RF-based tissue classification methods are not promising enough for clinical applications.